Multimodal Entity

Multimodal entity research focuses on understanding and processing entities represented across multiple data modalities, such as text and images, primarily aiming to improve entity linking, alignment, and recognition tasks. Current research emphasizes leveraging large language models (LLMs) and incorporating advanced techniques like optimal transport and graph neural networks to effectively fuse and reason over multimodal information, often addressing challenges such as missing or ambiguous data. This field is significant for advancing knowledge graph construction, multimodal information retrieval, and applications requiring robust understanding of entities within complex, real-world scenarios.